Disclosed herein are methods and systems of evaluating test-retest precision using fractional rank precision or mean-average precision, comprising: a) collecting a test and a retest result of each subject, wherein the results are described in feature space(s) and collected from a vision test machine; b) selecting, a first test result of a first subject; c) calculating distances from the first test result to the retest result of each subject; d) assessing, a similarity between the first test result and the retest result of each subject by ranking the distances in a non-descending order; e) assessing a rank precision for the first subject based on a rank of a distance from the first test result to the retest result of the first subject; f) repeating b), c), d), and e) for each subject; and evaluating, the test-retest precision based on the rank precision for each of the plurality of subjects.
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2. The system of claim 1, wherein the test comprises a first vision test.
3. The system of claim 2, wherein the retest result for the subject comprises a repeat of the first vision test after a therapy or treatment at a different time point.
The system is designed for vision testing and monitoring, particularly for tracking changes in a subject's vision over time, such as before and after therapeutic interventions. The system performs an initial vision test on a subject to establish a baseline measurement. The system then conducts a retest at a later time, after the subject has undergone a therapy or treatment, to assess changes in vision. The retest involves repeating the same vision test used initially, allowing for direct comparison of results. This enables evaluation of the effectiveness of the therapy or treatment by analyzing differences between the initial and retest results. The system may include additional components, such as a display for presenting test stimuli, input devices for subject responses, and processing units for analyzing and storing test data. The retest is performed at a distinct time point from the initial test, ensuring that any observed changes can be attributed to the therapy or treatment rather than natural variability. This approach is useful in clinical settings, research, or personalized medicine, where tracking vision changes due to interventions is critical.
4. The system of claim 2, wherein the first vision test comprises a vision acuity test, a contrast sensitivity function (CSF) test, or an optical coherence tomography (OCT) test.
5. The system of claim 1, wherein the one or more feature spaces are one-dimensional.
6. The system of claim 1, wherein the one or more feature spaces are multi-dimensional.
A system for processing data involves analyzing information within multi-dimensional feature spaces. These feature spaces represent structured or unstructured data in multiple dimensions, allowing for complex relationships and patterns to be identified. The system extracts relevant features from input data, such as text, images, or sensor readings, and maps them into these multi-dimensional spaces. By operating in multi-dimensional feature spaces, the system can capture intricate dependencies and correlations that may not be apparent in lower-dimensional representations. This approach enhances the accuracy and robustness of subsequent data processing tasks, such as classification, clustering, or anomaly detection. The system may also include preprocessing steps to normalize or transform the input data before mapping it into the feature spaces. Additionally, the system can adapt dynamically to changes in the data distribution or feature relevance, ensuring consistent performance over time. The use of multi-dimensional feature spaces enables the system to handle diverse and high-dimensional datasets effectively, improving the overall efficiency and reliability of data analysis.
7. The system of claim 1, wherein a feature of the feature space comprises: a median area under log of contrast sensitivity function (AULCSF) computed over the spatial frequency range of 1.5 to 6 cycles per degree (cpd), a median AULCSF computed over the spatial frequency range of 6 to 12 cpd, a median AULCSF computed over the spatial frequency range of 12 to 18 cpd, a median AULCSF computed over the spatial frequency range of 1. 5 to 18 cpd, a contrast sensitivity function (CSF) acuity, a parameter of CSF, a contrast sensitivity for at least one spatial frequency selected from 1, 1.5, 3, 6, 12, and 18 cpd, a peak sensitivity of the CSF, or a spatial frequency at which a CSF reaches a pre-determined contrast threshold.
8. The system of claim 1, wherein the rank comprises a real number ranging from 0 to N−1, wherein N represents a total number of subjects in the plurality of subjects.
9. The system of claim 1, wherein the distance comprises: a Euclidean distance, a Manhattan distance, or a Mahalanobis distance.
11. The system of claim 1, wherein the rank precision comprises an inverse of the rank of the retest result of the subject.
13. The system of claim 1, wherein the rank comprises a real number ranging from 1 to N, wherein N represent a total number of subjects of the plurality of subjects.
This invention relates to a ranking system for organizing a plurality of subjects, such as data entries, users, or items, based on a computed rank value. The system assigns a rank to each subject, where the rank is a real number between 1 and N, with N representing the total number of subjects. This allows for precise ordering and comparison of subjects, enabling applications such as personalized recommendations, search result prioritization, or performance evaluation. The ranking mechanism ensures that subjects are positioned in a continuous numerical scale, improving flexibility in sorting and filtering operations. The system may also include additional features, such as dynamic rank adjustment based on user interactions or contextual factors, to enhance accuracy and relevance. The invention addresses the challenge of efficiently organizing large datasets or user groups by providing a scalable and adaptable ranking framework.
14. The system of claim 1, wherein the vision test machine comprise: a computerized adaptive contrast sensitivity testing device, a quick contrast sensitivity function (qCSF) testing device, an optical coherence tomography (OCT) machine, a magnetic resonance imaging (MRI) machine, an ultrasound machine, a visual field testing machine, a fundus photography system, a dark adaptation measurement machine, an auto-refractor machine, a frequency-doubling threshold machine, a tonometer machine, an aberrometer machine, an eye-tracking device, or an ocular alignment machine.
16. The method of claim 15, wherein the test comprises a first vision test, wherein the retest result for the subject comprises a repeat of the first vision test after a therapy or treatment at a different time point, and wherein the first vision test comprises a vision acuity test, a contrast sensitivity function (CSF) test, or an optical coherence tomography (OCT) test.
17. The method of claim 15, wherein a feature of the feature space comprises: a median area under log of contrast sensitivity function (AULCSF) computed over the spatial frequency range of 1.5 to 6 cycles per degree (cpd), a median AULCSF computed over the spatial frequency range of 6 to 12 cpd, a median AULCSF computed over the spatial frequency range of 12 to 18 cpd, a median AULCSF computed over the spatial frequency range of 1.5 to 18 cpd, a contrast sensitivity function (CSF) acuity, a parameter of CSF, a contrast sensitivity for at least one spatial frequency selected from 1, 1.5, 3, 6, 12, and 18 cpd, a peak sensitivity of the CSF, or a spatial frequency at which a CSF reaches a pre-determined contrast threshold.
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December 30, 2019
November 8, 2022
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